Build Your First Machine Learning Project From Scratch

Ever wondered how platforms like Zomato or Swiggy predict your food delivery time? 🍔🏍️ In this video, we build a complete end-to-end Machine Learning project around that exact problem. 📌 CHAPTERS 00:00 Introduction 01:37 Basics of Machine Learning — Revision 08:49 Project Discussion / Converting Business Problem → ML Problem 15:00 Understanding the Dataset 35:50 Bad Data vs Good Data 44:56 Basic Data Cleaning 01:24:27 EDA — Explanation 01:33:40 EDA — Through Code 02:05:52 Feature Engineering — Explanation 02:17:00 Feature Engineering — Through Code 02:33:07 Encoding — Theory & Explanation 02:39:40 Train/Test Split — Explanation 02:41:16 Feature Scaling — Concept 02:49:20 Why Scale After the Train/Test Split? 02:58:02 Preprocessing — Code Walkthrough 03:06:20 Recap — Everything Covered So Far 03:09:18 What Is Scikit-Learn? 03:14:06 Model Building — Through Code 03:19:42 R² (Coefficient of Determination) 03:25:31 Adjusted R² 03:29:16 Model Evaluation — Code Walkthrough 03:45:40 Hyperparameter Tuning — Concept 03:57:27 Final Model — Code Walkthrough 04:08:00 Random Forest vs XGBoost 04:09:40 Adding a New Feature + Adjusted R² Evaluation 04:21:05 Saving the Model + Testing on New Data 04:25:44 Final Message ❤️ This isn't just another model.fit() tutorial. The goal is to understand why each step exists, how the pieces connect, and how an actual ML project goes from Raw Data → Insights → Features → ML Model.